This is a fundamental task in spatial data analysis, but as far as we are aware, has not comprehensively been studied as a stand-alone challenge.
Previous trajectory classification work includes [1]} which constructs some experiments similar to ours to demonstrate where their proposed technique is effective; we compare to and build on this work, but aim for a more objective and comprehensive comparative study. Other work considers subtrajectory [2]}, [3]} or segment [4]} classification but is not applicable to our tasks. For instance, [2]} builds a recurrent neural net (RNN) tuned to specific properties of their data sets – it centrally uses an encoded location id of each waypoint not available in general trajectory data sets, like the ones we consider. The focus of [4]} is on mode-of-flight identification from very short subsets of trajectories.
Another line of work is on inferring transportation modes [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]} which we directly compare against in Section REF .
Yet, the core trajectory classification task factors into many important challenges, and is destined to have an ever-expanding role as spatial data analysis increases in automation. For instance, given a GPS trace, can we determine which individual most likely made it, or what the mode of transportation was. Or does the trajectory represent favorable behavior (a healthy animal, a customer who will make a large purchase) or an unfavorable one (a diseased animal, a shoplifter).
